Self-assembling a 1,4-dioxane-degrading consortium and identifying the key role of Shinella sp. through dilution-to-extinction and reculturing

ABSTRACT Assembling a functional consortium and identifying novel degraders from contaminated environments are still challenging due to the large diversity of microorganisms and the difficulty in isolating pure cultures. Here, we constructed a relatively simple functional consortium by enriching 1,4-dioxane-degrading-consortia using a culture-dependent dilution-to-extinction (DTE) method and reported a new key dioxane-degrader Shinella sp. Our results showed that serial dilution and reculture led to a divergence in the degradation ability of each consortium. Next-generation sequencing data revealed that the divergence in degradation performance was due to the reassembly of microbiota in the DTE process, which occurred most notably in 10−8 and 10−9 dilutions. The shift in community structure at 10−9 prevented the recovery of 1,4-dioxane degradation capacity, and the newly dominant taxa, Xanthobacter and Acinetobacter, struggled to replace the original dominant genus Shinella for 1,4-dioxane biodegradation. Combining differential analysis of community structure and metabolic function, we confirmed that Shinella species have a stronger 1,4-dioxane degradation ability than Xanthobacter species in the enriched consortium. In addition, we verified our findings using our isolated dioxane-degrading bacteria, Shinella yambaruensis, resulting in the rapid recovery of degradation performance of a 10−9 dilution consortium with Xanthobacter and Acinetobacter as the dominant microbiota. Taken together, this study provides a strategy for self-assembling functional consortiums and identifying the key degraders to explore the underlying biological mechanisms of enriched contaminant-degrading consortia. IMPORTANCE Assembling a functional microbial consortium and identifying key degraders involved in the degradation of 1,4-dioxane are crucial for the design of synergistic consortia used in enhancing the bioremediation of 1,4-dioxane-contaminated sites. However, due to the vast diversity of microbes, assembling a functional consortium and identifying novel degraders through a simple method remain a challenge. In this study, we reassembled 1,4-dioxane-degrading microbial consortia using a simple and easy-to-operate method by combining dilution-to-extinction and reculture techniques. We combined differential analysis of community structure and metabolic function and confirmed that Shinella species have a stronger 1,4-dioxane degradation ability than Xanthobacter species in the enriched consortium. In addition, a new dioxane-degrading bacterium was isolated, Shinella yambaruensis, which verified our findings. These results demonstrate that DTE and reculture techniques can be used beyond diversity reduction to assemble functional microbial communities, particularly to identify key degraders in contaminant-degrading consortia.

Table S3.Monooxygenase encoded genes that potentially involved in 1,4-dioxane degradation in metagenome data.Only the genes that exhibited significant differences between groups (P < 0.05 for Deseq2) and were present in at least two samples were shown.Table S6.Alcohol dehydrogenase encoded genes that potentially involved in 1,4-dioxane degradation in metagenome data.Only the genes that exhibited significant differences between groups (P < 0.05 for Deseq2) and were present in at least two samples were shown.

Figure S1
Figure S1 Phenotype of microbial consortia growth in liquid medium, a comparison of -8 and 10 -9

Figure S2
Figure S2 Scanning electron microscopy (SEM) of the original microbial consortium and the 10 -9

Figure S5
Figure S5 Stacked bar charts of the most abundant 10 tax at the phylum (A) and order (B) level.The

Figure S6
Figure S6 Stacked bar charts of the most abundant 10 tax at the family level (A) and genus level (B).

Figure S7
Figure S7LEfSe (linear discriminant analysis effect size) analysis of the taxonomic differences among

Figure S8
Figure S8 Co-occurrence network based on spearman's correlation at the genus level.The size of the

Figure S9 Figure S10
Figure S9 Heat maps of weighted UniFrac distance matrices (A).PICRUSt2 function prediction profile

Figure S11
Figure S11Stacked bar charts of the most abundant 10 tax by metagenome analysis at the order (A) and

Figure S13
Figure S13The top 10 predominant genus in metagenome data.The remaining was categorized to

Figure S15
Figure S15 Number of genes (114333) assigned to KEGG pathway at Level1 and Level2.

Figure S16
Figure S16Heatmap of KEGG pathway level 1 (A) and 2 (B).Group E and F represent the dilution

Figure S21
Figure S21The top 10 species/features in metagenome and 16S rRNA sequences at genus level (A) and

Figure S22
Figure S22 Mantel test (999 permutations) for the correlation of metagenomic and 16S rRNA sequences.

Figure S23
Figure S23 PICRUSt2 predicted profiles (A) and metagenome functional profiles (B) in Level 1

Table S2 .
PICRUSt2 predicted monooxygenase encoded genes in KEGG Orthology(KO)database that are potentially involved in 1,4-dioxane degradation.The table presents the mean values of gene copies for each group (n=3).

Table S4 .
The betweenness centrality value of each node in the co-occurrence network.

Table S5 .
Aldehyde dehydrogenase encoded genes that potentially involved in 1,4-dioxane degradation in metagenome data.Only the genes that exhibited significant differences between groups (P < 0.05 for Deseq2) and were present in at least two samples were shown.